Bioinformatics 101

# Where am I?
pwd

# List files (long format, human-readable sizes)
ls -lh

# Move around
cd /path/to/directory
cd ..           # go up one level
cd ~            # go to home directory

# Create directories and files
mkdir -p project/data project/results project/scripts
touch README.md

# Copy, move, remove
cp file.txt backup.txt
mv file.txt new_name.txt
rm unwanted_file.txt         # CAREFUL: no recycle bin!
rm -r unwanted_directory/    # remove directory recursively

# View files
head -20 file.txt       # first 20 lines
tail -20 file.txt       # last 20 lines
less file.txt           # scroll through (press q to quit)
cat file.txt            # print entire file

# Count lines, words, characters
wc -l file.txt          # line count (critical for checking FASTQ!)

# Search inside files
grep "gene_name" results.txt            # find lines containing text
grep -c ">" sequences.fasta             # count FASTA sequences
grep -v "^#" file.vcf | head            # skip comment lines

# Column extraction and manipulation
cut -f1,3 file.tsv                      # extract columns 1 and 3
sort -k2,2n file.tsv                    # sort by column 2 numerically
awk '{print $1, $4-$3}' genes.bed       # compute column differences
sed 's/old/new/g' file.txt              # find-and-replace

# Pipes: chain commands together
cat reads.fastq | grep -c "^@"          # count FASTQ reads
zcat file.fastq.gz | head -8            # peek at gzipped files

# Make a script executable
chmod +x my_script.sh

# Connect to a remote server / HPC cluster
ssh username@cluster.university.edu

# Transfer files to/from server
scp local_file.txt username@cluster:~/data/
scp username@cluster:~/results/output.csv ./

# Transfer entire directories
rsync -avz local_dir/ username@cluster:~/project/

# Install Miniforge (recommended over full Anaconda)
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh
# Restart shell, then:

# Create an environment for RNA-Seq analysis
conda create -n rnaseq -c bioconda -c conda-forge \
  fastqc fastp star subread samtools multiqc

# Activate / deactivate
conda activate rnaseq
conda deactivate

# List all environments
conda env list

# Export environment for reproducibility
conda env export > rnaseq_env.yml

# Recreate on another machine
conda env create -f rnaseq_env.yml

#!/bin/bash
#SBATCH --job-name=my_analysis
#SBATCH --output=logs/%x_%j.out
#SBATCH --error=logs/%x_%j.err
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G
#SBATCH --time=12:00:00
#SBATCH --partition=normal
#SBATCH --mail-type=END,FAIL
#SBATCH --mail-user=your_email@university.edu

# Load your conda environment
source activate rnaseq

# Your analysis command
STAR --runThreadN 8 \
  --genomeDir /path/to/genome_index \
  --readFilesIn sample_R1.fastq.gz sample_R2.fastq.gz \
  --readFilesCommand zcat \
  --outSAMtype BAM SortedByCoordinate \
  --outFileNamePrefix results/sample_

echo "Job finished at: $(date)"

# Submit a job
sbatch my_script.sh

# Check your queued/running jobs
squeue -u $USER

# Cancel a job
scancel 12345678

# Check job efficiency after completion
seff 12345678

# Interactive session (for testing)
srun --cpus-per-task=4 --mem=16G --time=2:00:00 --pty bash

# Install Bioconductor (the bioinformatics R package repository)
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Install a package from Bioconductor
BiocManager::install("DESeq2")

# Install from CRAN
install.packages("tidyverse")

# Load packages
library(DESeq2)
library(tidyverse)

# Read data
df <- read.csv("my_data.csv")
df <- read.delim("my_data.tsv")   # tab-separated

# Basic data exploration
dim(df)       # rows x columns
head(df)      # first 6 rows
str(df)       # structure / types
summary(df)   # statistical summary

# The tidyverse way
df %>%
  filter(padj < 0.05) %>%
  arrange(desc(abs(log2FoldChange))) %>%
  head(20)

# Parse a FASTA file with Biopython
from Bio import SeqIO

for record in SeqIO.parse("genome.fasta", "fasta"):
    print(f"{record.id}: {len(record.seq)} bp")

# Read a count matrix with pandas
import pandas as pd
counts = pd.read_csv("counts.tsv", sep="\t", index_col=0)
print(counts.shape)
print(counts.head())